02510nam a2200241 a 450000500170000000800410001703500150005810000300007324501440010326000670024750001550031450200580046952013450052765000400187265000190191265000350193170000380196670000500200470000450205471000440209981000590214385600660220220260817162434.0190401s2015       uu m rtt   000 a|eng d  a.b1230251x0 aNatnaphat Subtaweepollert10aAnalysis of Standardized Precipitation Index with Remote Sensing Derived Drought Indices : A Case Study of South Lam Ham Pong Sub Watershed  aPathum Thani, Thailand :bAsian Institute of Technology,c2015  aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Remote Sensing and Geographic Information Systems  aThesis (M. Sc.) - Asian Institute of Technology, 2015  aDrought  is  a  natural  hazard  that  is  complicated  occurrence  and  difficult  to  prevent.  The  present study has a main objective to identify the appropriate remotely sensed drought index to assess the drought severity in non-station based areas. Thus, the Temperature Vegetation Index  (TVX),  Vegetation  Health  Index  (VHI) and Modified Perpendicular Drought Index (MPDI)  are  used  to  calculate  the  dryness  levels  of  South  Lam  Nam  Pong  sub-watershed.  These dryness values are compared with the Standardized Precipitation Index (SPI) values to  classify  the  drought  categories.  The  TVX,  VHI  and  MPDI  values  are  classified  as  a  moderately  drought  when  it  is  more  than  50.572,  less  than  55.746  and  less  than  1.178,  respectively.  In  addition,  the  time  lag  concept  is  considered  to  investigate  the  drought  evolution  by  comparing  the  SPI  one  month  time  scale  values  with  TVX,  VHI  and  MPDI  values. The results indicate that a trend line of TVX, VHI, MPDI have a best fit with a trend line of SPI one month time scale at five and seven month time lag. In conclusion, TVX is the best index to assess drought event in forest areas because it can be operated in a shorter time period than VHI and MPDI, and it show the highest correlation when compare with SPI values.  0aDrought forecastingxRemote sensing 0aRemote sensing 0aGeographic information systems0 aSarawut Ninsawat,eChairperson   1 aTripathi, Nitin Kumar,eExamination Committee1 aShrestha, Sangam,eExamination Committee2 aThailand (HM King),eScholarship donor 2 aAsian Institute of Technology.tThesis ;vno. RS-15-10  3Full-Textuhttp://203.159.5.9/ait-thesis/detail.php?q=B03914